Named Entity Recognition in Context
Colin Brisson, Ayoub Kahfy, Marc Bui, Frédéric Constant
Abstract
We present the Named Entity Recognition system developed by the Edit Dunhuang team for the EvaHan2025 competition. Our approach integrates three core components: (1) Pindola, a modern transformer-based bidirectional encoder pretrained on a large corpus of Classical Chinese texts; (2) a retrieval module that fetches relevant external context for each target sequence; and (3) a generative reasoning step that summarizes retrieved context in Classical Chinese for more robust entity disambiguation. Using this approach, we achieve an average F1 score of 85.58, improving upon the competition baseline by nearly 5 points.
Related Papers
From Roots to Rewards: Dynamic Tree Reasoning with RL2025-07-17HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals2025-07-17A Survey of Context Engineering for Large Language Models2025-07-17MCoT-RE: Multi-Faceted Chain-of-Thought and Re-Ranking for Training-Free Zero-Shot Composed Image Retrieval2025-07-17Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker2025-07-16Language-Guided Contrastive Audio-Visual Masked Autoencoder with Automatically Generated Audio-Visual-Text Triplets from Videos2025-07-16Context-Aware Search and Retrieval Over Erasure Channels2025-07-16Seq vs Seq: An Open Suite of Paired Encoders and Decoders2025-07-15